50 Days of LeetCode in Python: Algorithms Coding Interviews

Analyze the container with the most water problem by treating heights as vertical lines, width as index difference, and height as the minimum of two walls to compute area.

Use the brute force approach by testing every pair of walls, computing area as the minimum height times the index distance, and updating the maximum area with B1 and B2.

Explore the brute-force pseudocode for the container with most water, computing area as min height times width and updating the max area.

Shows how to replace brute force with a two-pointer approach to maximize container area. Move the smaller-height pointer and compute area as min(height left, height right) times width.

Walk through the two-pointer pseudocode to maximize container area using left and right pointers, area = min(height l, height r) × (r−l), moving the shorter pointer, O(n) time, O(1) space.

Implement the two-pointer approach in Python to solve the container with the most water by initializing a max area, computing min(height[l], height[r])*(r-l), and moving the smaller pointer until they meet.

Analyze the valid mountain array problem by checking for a strictly increasing prefix followed by a strictly decreasing suffix in arrays of length at least three.

Identify an increasing subsequence with a loop, then a decreasing subsequence, while tracking the end index to ensure the array ends after the sequence.

Walk through a pseudocode approach to detect a mountain array by an increasing subsequence followed by a decreasing subsequence, using an index and checks, with O(n) time and O(1) space.

Implement the code to check if a list of integers forms a valid mountain array, returning a boolean, by ascending then descending to reach the end.

Introduces the boats to save people problem from LeetCode, where at most two people per boat must sum to ≤ limit, and shows computing the minimum number of boats.

Sort the weights, then use two pointers to pair heaviest with lightest under the limit, counting boats; if they can't pair, send the heaviest alone.

This walkthrough presents a pseudocode solution for the boats problem, using sort and two pointers to pair weights under a limit and compute the number of boats.

Sort the array, use two pointers to pair lightest and heaviest within the weight limit, or assign the heaviest solo, yielding the minimum boats.

Move all zeros to the end of an array while preserving non-zero order, starting from brute force intuition and using a simple extra array in Python.

Walks through the brute force pseudocode for moving non-zero elements to an output array in their order, then appends zeros to maintain length, and analyzes time and space complexity.

Develop an in-place approach to move non-zero elements to the front using two pointers, I and J, then fill the remainder with zeros.

Walks through the optimal in-place solution for the move zeros problem: move non-zeros forward with a j-pointer, then fill the rest with zeros, in O(n+M) time and O(1) space.

Implement the in-place move zeros algorithm using a non-zero index to relocate non-zero elements while preserving their order, then fill the rest with zeros and submit to LeetCode.

Explore the longest substring without repeating characters in a string, including examples and edge cases. Note substrings are contiguous and distinguish them from subsequences; start with a brute force approach.

Explore the brute force method for longest substring without repeating characters by checking all substrings from each start, using a hash map to track seen chars and update max length.

Walk through the pseudocode for the longest substring without repeating characters, starting with a brute force left and right pointers and a seen map, then optimize with sliding window.

Explore the sliding window approach for the longest substring without repeating characters. Use two pointers and a map of last seen indices to update the window and track maximum length.

Walk through a sliding-window approach with left and right boundaries and a seen-characters map to track the longest window, updating the left boundary on repeats, with O(n) time and space.

Implement a function that returns the length of the longest unique substring using a sliding window and a hash map to track character positions.

Find the first and last positions of a target in a sorted array using brute force, by scanning left for the first occurrence and right for the last.

Demonstrate a brute force approach with find first and find last using forward and reverse scans to locate the target in an array, with O(N) time and O(1) space.

leverage binary search on a sorted array to find the first occurrence of a target, using left and right pointers and mid checks.

Walk through a binary search to find the first and last occurrences of a target in a sorted array, using two pointers, mid calculation, and boundary checks.

Walk through a pseudocode two-pointer binary search to find the last occurrence of a target, using left and right pointers with mid checks, achieving O(log n) time and O(1) space.

Implement the first and last position searches for a target in a sorted array using binary search in Python, returning [first, last] or [-1, -1].

Discover the first bad version problem with n versions and the isBadVersion API, then apply brute-force linear search to locate the first bad version, noting O(N) time and constant space.

Use the optimal solution by analyzing isBadVersion, locate the first bad version by halving the search range at each step, using the mid point, and returning the first bad index.

Demonstrates a binary search using a provided isBadVersion API to locate the first bad version with two pointers and a mid probe, yielding O(log n) time and O(1) space.

Implement binary search to identify the first bad version using left and right pointers, mid, and isBadVersion checks.

Identify the missing number in an array of n distinct values from zero to n. Note the constraints and examples that confirm a single missing number and non-empty inputs.

Explore the brute force approach to finding the missing number in a range by sorting the array and comparing consecutive elements to identify a gap, then return the missing number.

Explore approaches to find the missing number from zero to n, using a hash map for constant-time lookups to achieve linear time with a present numbers map.

Walk through a pseudo code using a present numbers map to locate the missing number in 0 to n, with o(n) time and o(n) space due to the map.

Apply Gauss's formula to compute the expected sum 0..n as n(n+1)/2, compare with the actual sum to reveal the missing number; implement with O(n) time and O(1) space.

Apply Gauss's sum formula to compute the total, tally the input numbers, and subtract to reveal the missing number, then submit to LeetCode and test via console.

Count primes less than a given number using a brute force approach that checks divisibility from two to n-1, and handle edge cases like n<2 and excluding the input.

Walk through a brute-force prime-checking pseudocode, counting primes below the input by nested loops that test divisibility, analyze time complexity O(n^2) and space O(1), and preview improvements.

Explore a sieve of Eratosthenes approach to find primes up to n by marking non-primes, starting from i squared, up to sqrt(n), and counting true entries.

Explore the sieve of Eratosthenes to identify primes up to 34 by marking multiples as non-prime, using an outer loop from two onward and breaking at the square root threshold.

Implement a sieve-style prime counter in python: handle n<2, initialize is_prime, mark 0 and 1 non-prime, loop to sqrt(n) marking multiples, then count primes and return.